Vassilis Sitokonstantinou, Mariza Kaskara, Iason Tsardanidis, Thanassis Drivas, Alexandros Marantos, Alkis Koukos, Haris Kontoes AgriHUB | Agriculture, Ecosystems and Environment Group

1. Analytics on Vegetation & Soil Index
time-series and DataCube End Point
service
Vassilis Sitokonstantinou, Mariza Kaskara, Iason Tsardanidis,
Thanassis Drivas, Alexandros Marantos, Alkis Koukos, Haris Kontoes
AgriHUB | Agriculture, Ecosystems and Environment Group
25-10-2022

2. Contents 01. The need of
geospatial analytics
03. Analytics on
Vegetation and Soil
Index Time-series
02. Analytics
functionalities
04. On-Demand
Access to the data

3. The possibility of
managing and
processing geospatial
big data to help
decision-making
therefore appears to be
an important scientific
and societal issue
01. Geospatial Analytics
However, it is difficult to
store, manage, process,
analyse, visualize and
extract useful information,
trends and knowledge
from geospatial big data
using traditional
approaches on local
machines.

4. • As ENVISION Data Cube is hosted on CreoDIAS, it has direct access to the full archive of Sentin
el images, without the need for downloading them.
• Automated and scheduled pipelines search for new satellite images, pre-process them and
generate resampled and re-projected Sentinel-1 and Sentinel-2 Cloud Optimized GeoTIFFs
• Metadata of these files are indexed to a PostgreSQL, while actual data are stored in CreoDIAS
VM
01. Geospatial Analytics
The DataCube Solution

5. ▪ All the required raw data
or indices are calculated
on a time window.
▪ The statistics of the
selected bands can be
provided either in the
form of aggregated
values for a parcel or as
a plot for a larger area.
Temporal Statistics
over an area
02. Analytics Functionalities
Visualizations can be extracted for any s1/s2 product, any aggregated statistic measurement, either
on the pixel or on the parcel level and for a specific parcel, a crop type or a crop family.
It plays a crucial role to the validation of results generated
by the rest of back- end processes

6. ▪ The functionality
considers the mean
value of cloud-free
pixels inside an area.
▪ As there can be gaps
between two or more
calculations due to
cloud presence, a
smoothing process
takes place.
▪ Thus, patterns can be
more noticeable and
reveal trends throughout
the years.
Smoothing
02. Analytics Functionalities

7. • This sub-task focus on
understanding possible
anomalies to any chosen
index.
• For example, NDVI
measures the greenness of
plant leaves, which
indicates an overall
vegetative health.
• As we have dense
measurements for NDVI,
there is the potential of
comparing current NDVI
value, either for a day or for
a month, to the average
computed NDVI over one or
more years.
Index Anomalies
02. Analytics Functionalities

8. Minimum Soil Cover
• GAEC 4 of current CAP demands the identification of
soil coverage during specific months throughout the
year.
• Initially, the average slope for each parcel has been
calculated based on a 20m raster Digital Elevation
Model.
• This slope refers to the full polygon, without using any
buffer zone.
• The algorithm of Geospatial Soil Sensing System
(GEOS3) has been utilized for creating soil masking
rules.
• GAEC 1 aims at the reduction of water pollution in nitra
te vulnerable areas has been developed, taking into
account the proximity into the closest water areas
• It relies on long-standing concept of soil erosion by
water, modelled through RUSLE.
• In addition, it utilizes also the orientation of each parcel
Runoff Risk Assessment
03. Analytics on Vegetation
and Soil Index Time-series

9. Stubble Burning Identification
• Through the identification of burnt crop parcels a
nd the date they were burnt, paying agencies can
monitor GAEC 6 compliance for each parcel.
• Farmers that follow this practice almost never dec
lare this action
• Pixel-based approach using dNBR index along
with sliding windows for national-scale coverage
• Checking pair of images for time instances t, t+1
and t, t+2.
• Burn ration for pixels falling into each parcel,
along with Burn Severity
03. Analytics on Vegetation and Soil Index Time-series

10. Natura 2000 Hotspot Detection
• Natura 2000 is a network of protected areas in the European Union aiming to assure the long-term survival of
Europe’s most valuable and threatened species and habitats.
• Same methodology as Stubble Burning, but using NDVI index.
• Marked pixels that fall inside Natura region and not matched with declared parcels from an LPIS are related to illegal
activity.
03. Analytics on Vegetation and Soil Index Time-series

11. API Service
• Data can be retrieved directly from the
datacube in the form of plots and
graphics.
• Users have the potential to construct
requests based on certain parameters
such as:
• Parcel ID
• Cloud Coverage
• Index
• Time range
• Buffer Zone
04. On-Demand Access to the data
…/api/parcels/{id}/{starting_date}/{ending_date}/{band}/{buffer}/{cfp}

12. API Service for raw data
• Data scientists and developers may need directly access to the data instead of getting images.
• Data Cube can be opened to authenticated users so to provide multidimensional data via xarrays for
a requested area, date range and series of bands or/and indices.
• This data are offered via the xpublish. Xpublish lets you easily publish Xarray Datasets via a REST API.
• Under the hood, Xpublish is using FastAPI.
• Efficient, on-demand delivery of large datasets may be enabled with Dask on the server-side.
04. On-Demand Access to the data

13. Thanks !